MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

World University root node

School of Mechanical Engineering

Engineering and Technology

School of Computer Engineering & Technology

Lecture Notes in Networks and Systems

Slider-Crank Four-Bar Mechanism-Based Ornithopter: Design and Simulation

In today’s competitive edge of the business world, there is a need for a systematic decision-making process in every field. The tourism field is one of the fast-growing filed in the current scenario which comprises a huge amount of information in an unstructured format which causes information overload. This field has diverse information and huge exposure at the national and international levels. To extract meaningful information and pass it on to the appropriate customer is a real challenge. Tourists in a current digitized scenario did not depend and trust on tourist place advertisement, travel guide, etc. rather they did their analysis by using the search engine. Getting correct traveling information using a traditional search engine is a quite tedious task. A recommendation system is the best information filtering system to get relevant and meaningful information based on an individual's needs with the help of artificial intelligence. A recommendation system focusing on the tourism domain will capture consumer travel interests and suggests a suitable place to visit. User’s travel history is the prime factor considered to recommend places that suits them. The source for knowledge acquisition on the tourism domain was gathered primarily from various sources like by surveying multiple users and through web scraping. We introduce various models and methods to provide recommendations to tourists. Our system also generates a dynamic number of recommendations to provide based on user selection. We propose a model based on a collaborative filtering approach that provides a worthy recommendation in the field of tourist field. In the existing system, people who want to travel have to search a lot of tourism websites. Also due to a lot of information available on the internet they might get confused. This problem is resolved by modules which recommend tourist places to people according to their interest. That is basically, it uses machine learning algorithms.&nbsp;

International Conference on Intelligent Computing in Information Technology for Engineering System International Conference on (ICICITES-2021)

Matrix Factorization Techniques for Tourists Recommendation System

In today’s world of data, large amount of homogenous as well as heterogeneous data is available in service sectors like hotel, tourists etc. Accessing helpful information from these large pool of data is quite tedious task. In developing country like India, Hotel sector is becoming mature now and retrieving and providing information to the users to select appropriate hotel from any unknown place is very crucial. An automated system like recommendation system is useful to provide help in decision making process of any person. Large amount of hotel data, scalability and efficiency problem of traditional service recommendation system leads to create a new hotel recommendation system. In this intelligent system personalised recommendations are provided to the user based on keywords which elaboratesuser’s choices and preferences [8].This system uses collaborative-filtering approach to generate recommendations using MapReduce parallel processing paradigm. Implementations is based on real time data related to hotels situated in Pune city

INFORMATION TECHNOLOGY IN INDUSTRY

HOTEL RECOMMENDATION SYSTEM BASED ON KASR SYSTEM

In today's digital world of information overload, the recommendation system plays an important role in the decision making of an individual as well as the collective societal level at large scale. The recommendation system is useful for leveraging the power of available data to create a better user experience and incremental revenue for the E-commerce companies. As per statistics from McKinsey about recommendation system gives 35 percent incremental revenue for Amazon, around 75 percent video consumption and 1billion $ saving for Netflix due to recommendations and around 60 percent of views on YouTube come from the leverage of powerful recommendations. This research paper shows, how a hybrid approach is useful to improve the prediction accuracy of the recommender system by using various matrix factorization techniques and regression on given sample data. Using all rating information from a large number of users and movies build a recommendation system that recommends/suggest a movie to concern user.

International Journal of Future Generation Communication and Networking 13 {\ldots}

A Hybrid Approach towards Improving Performance of Recommender System Using Matrix Factorization Techniques

A Pandemic engenders unusual challenge to conveyance of provision of medical care of our health. All these challenges are faced by workers which are limited in number. Covid-19 (Corona) becomes pandemic as it spreads throughout the world. With appearance of novel disease that covid-19 pandemic, whole world has facing critical situation from last few months ago. Now days, as every industry looks for an advance technique to observe/monitor and control the spreading of corona virus pandemic. So solution for this is the artificial intelligence supports to easy track with identification of the patient which are on high risk and easily useful in case of real time environment. AI technologies have high potential in improvement in planning, various treatments and reports about the covid-19 patients. Artificial intelligence is one of the important concept helps us to sort out those issues. Here this paper put the concept of how to maintain the safe distance to avoid the spreading of covid -19 ( SARS–CoV) virus using the artificial intelligence. This paper summarized all general concepts related to Artificial intelligence and Covid-19

International Journal of Grid and Distributed Computing

Artificial Intelligence Based Techniques To Prevent The Spread Of The Covid-19 Pandemic

In today’s digital era recommendation system is widely used applications from Netflix to Amazon, from Google to Goodreads, etc. Amazon estimated about 35% incremental revenue from product recommendations every year. Majority of e-commerce companies provides a recommendation system for purchasing product. The most search engine uses text-based search criteria for finding similar products. In this article, a comprehensive study of research contents related to text and image-based recommendation system is mentioned. First, we presented various text pre-processing techniques are used to clean dataset. Second, we described various techniques used for finding textual based similarity. Third, an advanced technique likeConvolution Neural Network(ConvNets) is used for computing image-based similarity of product. Cosine similarity is calculated when word occurrence is important in textual input whereas Euclidean distance is measured when an image or semantic-based text is considered as input. Experimental results depict how recommendations using ConvNets is more exciting than usual classical techniques.

International Journal of Advanced Science and Technology

Visual Similarity Using Convolution Neural Network over Textual Similarity in Content- Based Recommender System

Plants is the heart of an agriculture field and it’s become an important source of energy as well as financial income. The leaves of a plant provides the most important information or data which provides us to know which type of plant it is and which type of disease is infected on that leaf. The plants have to be monitored all the time in order to detect the first symptoms of a disease before it is spread to the whole crop. Professional agriculture engineers may not be available to continuously monitor a crop. Remote monitoring through machine vision can offer an alternative option. After analyzing these different techniques as well as different algorithms, and identifying their limitations, we conclude with several promising directions for future research.&nbsp;

International Journal of Research and Analytical Reviews in Applied Science and Engineering Technology

Farmer Buddy-Plant Leaf Disease Detection on Android Phone

Extensive use of data and increasing sales by implementing recommender system by various retail and e-commerce companies through their website is tremendously boosted in current scenario. Recommendation systems is a software application that provide suggestions to the intended user regarding which products is buy and which is not to buy. This systems uses various filtering techniques to provide recommendations to the customers. Filtering techniques that helps in decision making of customers are most likely content-based filtering, CNN model filtering and hybrid filtering. In order to reduce information search time of user it is need to integrate the recommended facility in E-Commerce. Content-based filtering is used due to its suitability in the domain or in situations where the products are more than users. IDF Weighted word to vector model were used to determine whether a product is relevant or similar to a user's profile of interest. CNN model is used to find out image based similarities and both of this technologies integrated as a hybrid system and finally system give recommendation based on hybrid filtering system. This paper also gives an importance of an algorithm for providing recommendations or suggestions based on queries of users. Algorithms employ both IDF Weighted Word to vector model and CNN model. This review gives an overview of available data sets, methods for preprocessing data sets, recommendations techniques and challenges involved.

Journal of Emerging Technologies and Innovative Research

Survey of Classical and Advanced Techniques for Product Recommendations

Various operations performed by waiters like starting from taking orders till delivery of food/menu to the customer, also billing by cashier made manually. Due to manual process and paperwork may cause time delay, ignorance of customer, errors in billing leads to dissatisfaction of customers. As in today's digital era, customers expect high quality, smart services from restaurant. So to improve quality of service and to achieve customer satisfaction, we proposed improvised E-Menu Recommendation System. This system can build e-reputation of restaurant and customer community in live. All orders and expenses are stored in database and give statistics for expenses and profit. The proposed recommender system uses wireless technology and menu recommender to build improvised E-Menu Recommendation System for customer-centric service. Professional feels and environment are provided to the customers/delegates with additional information about food/menu by using interactive graphics. Outcomes of experimental are obtained by comparing results of two data mining algorithms Apriori and FP-growth which have practical potential in providing customer-centric service.

Advances in Intelligent Systems and Computing

Developing an improvised E-menu recommendation system for customer

Wireless location finding is one of the key technologies for wireless sensor network. GPS is the technology used but it can be used for the outdoor locations. When we deal with the indoor location GPS does not work. Indoor locations include buildings like supermarkets, big malls, parking spaces, universities, etc. In this area the accuracy of the GPS location is greatly reduced. Location showed on the map in not correct when the GPS is used under the indoor environments. But for the indoor localization it requires the higher accuracy and the special purpose GPS is not feasible for the current view. And also when GPS is used in the mobile device it consumes a lot of the mobile battery to run the application which causes the drainage of the mobile battery within some hours. So to find out the accurate location for indoor environment we use the RSSI-based trilateral localization algorithm. The algorithm has the low cost and the algorihm does not require any additional hardware support and moreover the algorithm is easy to understand. The algorithm consumes very less battery as compared the battery consumption of the GPS. Hence this algorithm has become the mainstream localization algorithm in the wireless sensor network. With the development of the wireless sensor network and the smart devices the WIFI access points are also increasing. The mobile smart device detects three or more known WIFI hotdpot position. And using the values from the WIFI routers it calculates the current location of the mobile device. Under the indoor environment and can navigate to the destination using the navigation function and also can enable the low consumption of the smart mobile battery for the tracking purpose.

Indoor Wireless Tracking Using Wi-Fi

The use of mobilе devicеs in combination with the rapid growth of the internеt has generatеd an information ovеrload problеm. Recommendеr systеms are a necеssity to decidе which of the data are relеvant to the user. Howevеr, in mobilе devicеs therе are differеnt factors who are crucial to information retriеval, such as the location, the screеn sizе and the procеssor speеd. This papеr givеs an overviеw of the technologiеs relatеd to mobilе recommendеr systеms and a morе detailеd dеscription of the challengеs facеd.

INTERNATIONAL JOURNAL OF SCIENTIFIC PROGRESS AND RESEARCH

Enhanced Role-Based Access Control Model for Mobile Recommender System. International Journal of Scientific Progress & Research Engineering

The use of mobilе devicеs in combination with the rapid growth of the internеt has generatеd an information ovеrload problеm. Recommendеr systеms are a necеssity to decidе which of the data are relеvant to the user. Howevеr, in mobilе devicеs therе are differеnt factors who are crucial to information retriеval, such as the location, the screеn sizе and the procеssor speеd. This papеr givеs an overviеw of the technologiеs relatеd to mobilе recommendеr systеms and a morе detailеd dеscription of the challengеs facеd